As the number of vehicles in use increases every year, the amount of insurance services of insurance companies has also been increasing. Thus, at present, a key industry research direction of various vehicle models is how to quickly and accurately provide users with vehicle damage assessment services.
During vehicle damage assessment, a damaged automotive part of a vehicle usually needs to be determined by recognizing a damage assessment image. The accuracy of recognizing a damaged automotive part mainly depends on an algorithm/model for damage assessment image recognition. A damage location and an extent of damage are obtained by recognizing vehicle damage images (including image and video materials such as pictures and videos) by using various models/algorithms. Then a damage assessment result is obtained according to a corresponding maintenance and repair policy. At present, a model/algorithm used in the industry mainly collects appearance data of various vehicle models in advance, and then uses a constructed automotive part damage algorithm to recognize a damaged automotive part and an extent of damage in the damage assessment image. To ensure recognition precision, as many as possible appearance image data of various vehicles are usually obtained as sample images for training. The period of a model algorithm training and parameter optimization process is usually relatively long, and overall implementation costs are relatively high. In addition, by recognizing a damaged automotive part in an image by purely relying on a model algorithm, the accuracy of recognizing a part is also limited to the amount of collected vehicle appearance image data. Therefore, during vehicle damage assessment image recognition, a processing solution with lower implementation costs and more accurate recognition result is needed.